Introduction: Over the past decades, billions of people on Earth have used respirator masks to prevent animal-to-human and human-to-human virus transmission. Recent research has shown the low risk of surface transmission of COVID-19, which turned into a pandemic since January 2020. Social distancing and the use of masks indoors are the most important factors in breaking its transmission chain.Material and Methods: However, the use of contaminated respirator masks can cause dangerous microbial and viral diseases. By adding the factor “avoiding microbial contamination”, the proposed model, called “Excellent Performance by Avoiding Microbial Contamination (EPBAMC)”, improves the WHO’s three-factor optimal-performance model of the respirator masks. In this study, to evaluate the need to add the factor of “avoiding contamination”, samples of brand-new respirator masks were collected from several countries and their microbial contamination was carefully studied. The research method was such that the research steps were performed with highest accuracy rate and no double infection was created.Results: By culturing in sterilized medium, the bacterial load of the respirator masks was studied and the results were analyzed. By performing different cultures, a variety of pathogenic microorganisms were identified on half of the respirator mask samples. Some brand-new respirator mask samples contained more than one pathogen. A very important issue was that bacteria were found in brand-new respirators distributed by pharmacies that cause nosocomial infections and are resistant to antibiotics. Conclusion: The results of this study made it necessary to review the standards of the production and distribution process and the procedures for controlling and inspecting respirator masks.
{"title":"Excellent Performance by Avoiding Microbial Contamination (EPBAMC): A New Portal and Model for Safety of Respirator Masks Approved by Bacterial Contamination Field Research","authors":"Navid Hashemi Taba, A. Khatavakhotan","doi":"10.30699/fhi.v11i1.350","DOIUrl":"https://doi.org/10.30699/fhi.v11i1.350","url":null,"abstract":"Introduction: Over the past decades, billions of people on Earth have used respirator masks to prevent animal-to-human and human-to-human virus transmission. Recent research has shown the low risk of surface transmission of COVID-19, which turned into a pandemic since January 2020. Social distancing and the use of masks indoors are the most important factors in breaking its transmission chain.Material and Methods: However, the use of contaminated respirator masks can cause dangerous microbial and viral diseases. By adding the factor “avoiding microbial contamination”, the proposed model, called “Excellent Performance by Avoiding Microbial Contamination (EPBAMC)”, improves the WHO’s three-factor optimal-performance model of the respirator masks. In this study, to evaluate the need to add the factor of “avoiding contamination”, samples of brand-new respirator masks were collected from several countries and their microbial contamination was carefully studied. The research method was such that the research steps were performed with highest accuracy rate and no double infection was created.Results: By culturing in sterilized medium, the bacterial load of the respirator masks was studied and the results were analyzed. By performing different cultures, a variety of pathogenic microorganisms were identified on half of the respirator mask samples. Some brand-new respirator mask samples contained more than one pathogen. A very important issue was that bacteria were found in brand-new respirators distributed by pharmacies that cause nosocomial infections and are resistant to antibiotics. Conclusion: The results of this study made it necessary to review the standards of the production and distribution process and the procedures for controlling and inspecting respirator masks.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131888342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Senait Samuel Bramo, Amare Desta Mamo, Munavvar Syedda
Introduction: Health care system is information-driven sector where Health Care Providers (HCPs) regularly deliver comprehensive health services based on the available, accessible, and reliable health information. However, there is lack of empirical evidence about the culture of current state of health information needs; sources and channels that used by the health professionals in Primary Level Health Care (PLHC) of Ethiopia. Thus, this study aimed to explore the health information needs, sources, and channels used by the health professionals in PLHC in Wolaita zone, South Ethiopia based on the information seeking and behavior need model.Material and Methods: Ethnographic study design was employed using participant observation and key informant in-depth interviews as a data collection method. Observation and interviews data were entered on Qualitative Data Analysis mine software version. The quotes and field notes were summarized and linked to the information seeking and behavior need model to generate new meaning. Results: Consequently, HCPs demonstrated their needs of health promotive and disease preventive health information as compared to health information focusing on early diagnosis and treatment. The major purpose was to answer colleagues and patients’ question. The unpredictability of the health conditions and associated HCPs skepticism was a major precursor for a deliberate search of health information. Although it is pigeonholing HCPs in PLHC settings preferred formal channels of information and resources held in and delivered in digital format using mobile, computer, and Internet as compared to print and human sources. Furthermore, the absence of library or resource center, shortage of ICT infrastructure, and poor information literacy skill were raised as reasons for unmet health information need in PLHC settings.Conclusion:Thus, this study showed that the need for formal channels of information and suggests the establishment of reading/resource corners/centers and design, development, and implementation of information literacy module for HCPs in PLHC.
{"title":"The Current State of Information Needs, Sources and Channels Used by the Health Care Providers in Primary Level Health Care, Ethiopia: Ethnographic Study","authors":"Senait Samuel Bramo, Amare Desta Mamo, Munavvar Syedda","doi":"10.30699/fhi.v11i1.338","DOIUrl":"https://doi.org/10.30699/fhi.v11i1.338","url":null,"abstract":"Introduction: Health care system is information-driven sector where Health Care Providers (HCPs) regularly deliver comprehensive health services based on the available, accessible, and reliable health information. However, there is lack of empirical evidence about the culture of current state of health information needs; sources and channels that used by the health professionals in Primary Level Health Care (PLHC) of Ethiopia. Thus, this study aimed to explore the health information needs, sources, and channels used by the health professionals in PLHC in Wolaita zone, South Ethiopia based on the information seeking and behavior need model.Material and Methods: Ethnographic study design was employed using participant observation and key informant in-depth interviews as a data collection method. Observation and interviews data were entered on Qualitative Data Analysis mine software version. The quotes and field notes were summarized and linked to the information seeking and behavior need model to generate new meaning. Results: Consequently, HCPs demonstrated their needs of health promotive and disease preventive health information as compared to health information focusing on early diagnosis and treatment. The major purpose was to answer colleagues and patients’ question. The unpredictability of the health conditions and associated HCPs skepticism was a major precursor for a deliberate search of health information. Although it is pigeonholing HCPs in PLHC settings preferred formal channels of information and resources held in and delivered in digital format using mobile, computer, and Internet as compared to print and human sources. Furthermore, the absence of library or resource center, shortage of ICT infrastructure, and poor information literacy skill were raised as reasons for unmet health information need in PLHC settings.Conclusion:Thus, this study showed that the need for formal channels of information and suggests the establishment of reading/resource corners/centers and design, development, and implementation of information literacy module for HCPs in PLHC.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126431316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vinu Sherimon, P. Sherimon, Rahul V. Nair, Renchi Mathew, Sandeep M. Kumar, Khalid Shaikh, Hilal Khalid Al Ghafri, Huda Salim Al Shuaili
Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman.Materials and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.
{"title":"eCOVID19 – Development of Ontology-based Clinical Decision Support System for COVID-19","authors":"Vinu Sherimon, P. Sherimon, Rahul V. Nair, Renchi Mathew, Sandeep M. Kumar, Khalid Shaikh, Hilal Khalid Al Ghafri, Huda Salim Al Shuaili","doi":"10.30699/fhi.v11i1.339","DOIUrl":"https://doi.org/10.30699/fhi.v11i1.339","url":null,"abstract":"Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman.Materials and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122814269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Moradi, Shadi Gholizade, Reyhaneh Rostami, F. Moghbeli
Introduction: Nurses and medical staff and health technologists as the largest segment of the health system are the main users of health information systems that understanding the perspective and how to use this system can be effective in improving the quality of community health. The aim of this study was to evaluate the performance of the Sib system of health centers in Bojnourd and Neishabour.Material and Methods: This is an applied study and was performed by descriptive cross-sectional method. The study population included all users of the Sib system in the health centers of Bojnourd and Neishabour who used the Sib system. Sampling was available and data were collected using a researcher-made questionnaire and data were analyzed using SPSS software version 21.Results: According to the findings of the study, the majority of users were 70% female and 30% male, 58% were in the age group of 30-39 years, and 40% of them had 5-9 years of work experience and also 63% of System users have a bachelor's degree. In the technical field, from the point of view of 40% of users, the ease of using the system is moderate.Conclusion: Based on the identified factors, by strengthening the advantages of the system and also trying to eliminate or reduce the shortcomings in it, it is possible to institutionalize and use the system more practically in order to solve health problems.
{"title":"Evaluating the Performance of the SIB System of Health Centers in Bojnourd and Neishabour from the Users Perspective in 2020","authors":"G. Moradi, Shadi Gholizade, Reyhaneh Rostami, F. Moghbeli","doi":"10.30699/fhi.v11i1.333","DOIUrl":"https://doi.org/10.30699/fhi.v11i1.333","url":null,"abstract":"Introduction: Nurses and medical staff and health technologists as the largest segment of the health system are the main users of health information systems that understanding the perspective and how to use this system can be effective in improving the quality of community health. The aim of this study was to evaluate the performance of the Sib system of health centers in Bojnourd and Neishabour.Material and Methods: This is an applied study and was performed by descriptive cross-sectional method. The study population included all users of the Sib system in the health centers of Bojnourd and Neishabour who used the Sib system. Sampling was available and data were collected using a researcher-made questionnaire and data were analyzed using SPSS software version 21.Results: According to the findings of the study, the majority of users were 70% female and 30% male, 58% were in the age group of 30-39 years, and 40% of them had 5-9 years of work experience and also 63% of System users have a bachelor's degree. In the technical field, from the point of view of 40% of users, the ease of using the system is moderate.Conclusion: Based on the identified factors, by strengthening the advantages of the system and also trying to eliminate or reduce the shortcomings in it, it is possible to institutionalize and use the system more practically in order to solve health problems.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125489704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: Heart disease is often associated with conditions such as clogged arteries due to the sediment accumulation which causes chest pain and heart attack. Many people die due to the heart disease annually. Most countries have a shortage of cardiovascular specialists and thus, a significant percentage of misdiagnosis occurs. Hence, predicting this disease is a serious issue. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for disease prediction.Material and Methods: Several algorithms were utilized to predict heart disease among which Decision Tree, Random Forest and KNN supervised machine learning are highly mentioned. The algorithms are applied to the dataset taken from the UCI repository including 294 samples. The dataset includes heart disease features. To enhance the algorithm performance, these features are analyzed, the feature importance scores and cross validation are considered.Results: The algorithm performance is compared with each other, so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, Accuracy, AUC ROC are 83% and 99% respectively for Decision Tree algorithm. Logistic Regression algorithm with accuracy and AUC ROC are 88% and 91% respectively has better performance than other algorithms. Therefore, these techniques can be useful for physicians to predict heart disease patients and prescribe them correctly.Conclusion: Machine learning technique can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the prediction of heart disease is compared to determine the most appropriate classification. As a result of evaluation, better performance was observed in both Decision Tree and Logistic Regression models.
{"title":"Comparison of the Performance of Machine Learning Algorithms in Predicting Heart Disease","authors":"Sajad Yousefi","doi":"10.30699/fhi.v10i1.349","DOIUrl":"https://doi.org/10.30699/fhi.v10i1.349","url":null,"abstract":"Introduction: Heart disease is often associated with conditions such as clogged arteries due to the sediment accumulation which causes chest pain and heart attack. Many people die due to the heart disease annually. Most countries have a shortage of cardiovascular specialists and thus, a significant percentage of misdiagnosis occurs. Hence, predicting this disease is a serious issue. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for disease prediction.Material and Methods: Several algorithms were utilized to predict heart disease among which Decision Tree, Random Forest and KNN supervised machine learning are highly mentioned. The algorithms are applied to the dataset taken from the UCI repository including 294 samples. The dataset includes heart disease features. To enhance the algorithm performance, these features are analyzed, the feature importance scores and cross validation are considered.Results: The algorithm performance is compared with each other, so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, Accuracy, AUC ROC are 83% and 99% respectively for Decision Tree algorithm. Logistic Regression algorithm with accuracy and AUC ROC are 88% and 91% respectively has better performance than other algorithms. Therefore, these techniques can be useful for physicians to predict heart disease patients and prescribe them correctly.Conclusion: Machine learning technique can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the prediction of heart disease is compared to determine the most appropriate classification. As a result of evaluation, better performance was observed in both Decision Tree and Logistic Regression models.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115971755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Firouzkouhi, Abdolghani Abdollahimohammad, Judie Arulappan, T. Nouraei, J. Farzi
Introduction: Telenursing during the COVID-19 pandemic with an emphasis on self-care is an effective approach to help patients, hospitals, as well as community. Despite the many challenges and benefits, tele-nursing can be used to help COVID 19 patients with new technologies. This study aimed to explore the challenges and opportunities of using tele-nursing in the COVID 19 Pandemic for helping patients with COVID 19 to gain better care.Material and Methods: An integrative review was conducted from December, 2019 to January, 2021. Databases of PubMed, MEDLINE, Web of Science, Scopus, CINHAL, and google scholar were searched on the concept of tele-nursing by using the following keywords, of COVID-19, Coronavirus, Telenursing, nurse roles, technology, Pandemics and Internet. DaA ta were analyzed according to Broome method.Results: The main results of tele-nursing in COVID 19 includes: implementation problems, insurance coverage, prevention of nurses, the problem of continuing care, and changing the roles of nurses’ infections, development of nursing knowledge, the emergence of technological care providing, emphasis on patient independence and transmission cycle control.Conclusion: Tele-nursing, this, despite the challenges, has many benefits that are effective in the current situation and effective, and reliable measure, through effective planning and implementation, help control COVID-19.
{"title":"Challenges and Opportunities of Using Telenursing During COVID-19 Pandemic: An Integrative Review","authors":"M. Firouzkouhi, Abdolghani Abdollahimohammad, Judie Arulappan, T. Nouraei, J. Farzi","doi":"10.30699/fhi.v10i1.332","DOIUrl":"https://doi.org/10.30699/fhi.v10i1.332","url":null,"abstract":"Introduction: Telenursing during the COVID-19 pandemic with an emphasis on self-care is an effective approach to help patients, hospitals, as well as community. Despite the many challenges and benefits, tele-nursing can be used to help COVID 19 patients with new technologies. This study aimed to explore the challenges and opportunities of using tele-nursing in the COVID 19 Pandemic for helping patients with COVID 19 to gain better care.Material and Methods: An integrative review was conducted from December, 2019 to January, 2021. Databases of PubMed, MEDLINE, Web of Science, Scopus, CINHAL, and google scholar were searched on the concept of tele-nursing by using the following keywords, of COVID-19, Coronavirus, Telenursing, nurse roles, technology, Pandemics and Internet. DaA ta were analyzed according to Broome method.Results: The main results of tele-nursing in COVID 19 includes: implementation problems, insurance coverage, prevention of nurses, the problem of continuing care, and changing the roles of nurses’ infections, development of nursing knowledge, the emergence of technological care providing, emphasis on patient independence and transmission cycle control.Conclusion: Tele-nursing, this, despite the challenges, has many benefits that are effective in the current situation and effective, and reliable measure, through effective planning and implementation, help control COVID-19.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122450955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Abbasi, Reza Khajouei, Monireh Sadeghi Jabali, M. Mirzaei
Introduction: One of the well-known problems related to the information quality is the information incompleteness in health information systems. The purpose of this study was to investigate the completeness rate of patients’ information recorded in the hospital information system, sending information from which to Iranian electronic health record system (SEPAS) seemed to be unsuccessful.Methods: This study was conducted in six hospitals associated with Kerman University of Medical Sciences (KUMS) in Iran. In this study, 882 records which had failed to be sent from three hospital information systems to SEPAS were reviewed and the data were collected using a checklist. Data were analyzed using the descriptive and inferential statistics with SPSS.18.Results: A total of 18758 demographic and clinical information elements were examined. The rate of completeness was 55%. The highest completeness rate of demographic information was related to name, surname, gender, nationality, date of birth, father's name, marital status, place of residence, telephone number (79-100%), and in clinical information it was related to the final diagnosis (74%). The completeness rate of some information elements was significantly different among the hospitals (p <0.05). The completeness rate of information communicated to the Iranian national electronic health record was at a moderate level.Conclusion: This study showed that completeness rate is different among hospitals using the same hospital information system. The results of this study can help the health policymakers and developers of the national electronic health record in developing countries to improve completeness rate and also information quality in health information systems.
{"title":"Data Incompleteness Preventing Information Communication from Hospital Information Systems to the Iranian National Electronic Health Record (SEPAS)","authors":"R. Abbasi, Reza Khajouei, Monireh Sadeghi Jabali, M. Mirzaei","doi":"10.30699/fhi.v10i1.320","DOIUrl":"https://doi.org/10.30699/fhi.v10i1.320","url":null,"abstract":"Introduction: One of the well-known problems related to the information quality is the information incompleteness in health information systems. The purpose of this study was to investigate the completeness rate of patients’ information recorded in the hospital information system, sending information from which to Iranian electronic health record system (SEPAS) seemed to be unsuccessful.Methods: This study was conducted in six hospitals associated with Kerman University of Medical Sciences (KUMS) in Iran. In this study, 882 records which had failed to be sent from three hospital information systems to SEPAS were reviewed and the data were collected using a checklist. Data were analyzed using the descriptive and inferential statistics with SPSS.18.Results: A total of 18758 demographic and clinical information elements were examined. The rate of completeness was 55%. The highest completeness rate of demographic information was related to name, surname, gender, nationality, date of birth, father's name, marital status, place of residence, telephone number (79-100%), and in clinical information it was related to the final diagnosis (74%). The completeness rate of some information elements was significantly different among the hospitals (p <0.05). The completeness rate of information communicated to the Iranian national electronic health record was at a moderate level.Conclusion: This study showed that completeness rate is different among hospitals using the same hospital information system. The results of this study can help the health policymakers and developers of the national electronic health record in developing countries to improve completeness rate and also information quality in health information systems.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128697257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: On March 20, 2020, the World Health Organization (WHO) announced the spread of SARS-CoV-2 infection in most countries worldwide as a pandemic. COVID-19 is mainly disseminated through human-to-human transmission route via direct contact and respiratory droplets. Telehealth and/or telemedicine technologies are beneficial methods that could be employed to deal with pandemic situation of communicable infections. The purpose of this proposed systematic review study is to sum up the functionalities, applications, and technologies of telemedicine during COVID-19 outbreak.Material and Methods: This review will be carried out in accordance with the Cochrane Handbook and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. PubMed and Scopus databases were searched for related articles. Randomized and non-randomized controlled trials published in English in scientific journals were identified to be evaluated for eligibility. Articles conducted on telemedicine services (TMS) during COVID-19 outbreak (2019-2020) were identified to be evaluated.Results: The literature search for related articles in PubMed and Scopus databases led to the identification and retrieval of a total of 1118 and 485 articles, respectively. After eliminating duplicate articles, title and abstract screening process was performed for the remaining 1440 articles. The current study findings are anticipated to be used as a guide by researchers, decision makers, and managers to design, implement, and assess TMS during COVID-19 crisis.Conclusion: As far as we know, this systematic review is conducted to comprehensively evaluate TM methods and technologies developed with the aim of controlling and managing COVID-19 pandemic. This study highlights important applications of telemedicine in pandemic conditions, which could be employed by future health systems in controlling and managing communicable infections when an outbreak occurs.
{"title":"Protocol of a Systematic Review on Telemedicine Solutions in COVID-19 Pandemic","authors":"S. Eslami, Raheleh Ganjali","doi":"10.30699/fhi.v10i1.317","DOIUrl":"https://doi.org/10.30699/fhi.v10i1.317","url":null,"abstract":"Introduction: On March 20, 2020, the World Health Organization (WHO) announced the spread of SARS-CoV-2 infection in most countries worldwide as a pandemic. COVID-19 is mainly disseminated through human-to-human transmission route via direct contact and respiratory droplets. Telehealth and/or telemedicine technologies are beneficial methods that could be employed to deal with pandemic situation of communicable infections. The purpose of this proposed systematic review study is to sum up the functionalities, applications, and technologies of telemedicine during COVID-19 outbreak.Material and Methods: This review will be carried out in accordance with the Cochrane Handbook and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. PubMed and Scopus databases were searched for related articles. Randomized and non-randomized controlled trials published in English in scientific journals were identified to be evaluated for eligibility. Articles conducted on telemedicine services (TMS) during COVID-19 outbreak (2019-2020) were identified to be evaluated.Results: The literature search for related articles in PubMed and Scopus databases led to the identification and retrieval of a total of 1118 and 485 articles, respectively. After eliminating duplicate articles, title and abstract screening process was performed for the remaining 1440 articles. The current study findings are anticipated to be used as a guide by researchers, decision makers, and managers to design, implement, and assess TMS during COVID-19 crisis.Conclusion: As far as we know, this systematic review is conducted to comprehensively evaluate TM methods and technologies developed with the aim of controlling and managing COVID-19 pandemic. This study highlights important applications of telemedicine in pandemic conditions, which could be employed by future health systems in controlling and managing communicable infections when an outbreak occurs.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122490482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mahdi Montazeri, Reza Khajouei, E. Mohajeri, L. Ahmadian
Introduction: One way to reduce medication errors in the cardiovascular settings is to electronically prescribe medication through the computerized physician order entry system (CPOE). Improper design and non-compliance with users' needs are obstacles to implementing this system. Therefore, it is necessary to consider the standard minimum data set (MDS) of this system in order to meet the basic needs of its users. The aim of this study was to introduce MDS in the cardiovascular CPOE drug system to standardize data items as well as to facilitate data sharing and integration with other systems.Material and Methods: This study was a survey study conducted in 1399 in Iran. The study population was all cardiologists in Iran. The data collection tool was a researcher-made questionnaire consisting of 33 questions. Data were analyzed in SPSS-24 using descriptive statistics.Results: A total of 31 cardiologists participated in this study. The participants identified 19 of the 25 drug data items as essential for drug MDS. Five data items (Medication name, Medication dosage, Medication frequency, Medication start date and Patient medication history) were considered essential by more than 90% of the participants.Conclusion: The results of this study identified drug MDS for the cardiovascular CPOE system. The results of this study can be a model for CPOE system designers to develop new systems or upgrade existing systems.
{"title":"Development of a Minimum Data Set for Drug Module of Computerized Physician Order Entry System","authors":"Mahdi Montazeri, Reza Khajouei, E. Mohajeri, L. Ahmadian","doi":"10.30699/fhi.v10i1.323","DOIUrl":"https://doi.org/10.30699/fhi.v10i1.323","url":null,"abstract":"Introduction: One way to reduce medication errors in the cardiovascular settings is to electronically prescribe medication through the computerized physician order entry system (CPOE). Improper design and non-compliance with users' needs are obstacles to implementing this system. Therefore, it is necessary to consider the standard minimum data set (MDS) of this system in order to meet the basic needs of its users. The aim of this study was to introduce MDS in the cardiovascular CPOE drug system to standardize data items as well as to facilitate data sharing and integration with other systems.Material and Methods: This study was a survey study conducted in 1399 in Iran. The study population was all cardiologists in Iran. The data collection tool was a researcher-made questionnaire consisting of 33 questions. Data were analyzed in SPSS-24 using descriptive statistics.Results: A total of 31 cardiologists participated in this study. The participants identified 19 of the 25 drug data items as essential for drug MDS. Five data items (Medication name, Medication dosage, Medication frequency, Medication start date and Patient medication history) were considered essential by more than 90% of the participants.Conclusion: The results of this study identified drug MDS for the cardiovascular CPOE system. The results of this study can be a model for CPOE system designers to develop new systems or upgrade existing systems.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131261780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Abdolahi, V. Nowzari, A. Pirzad, S. Amirhosseini
Introduction: Health companies need investment for development. Due to the high risk of their activities, it is very difficult to attract investment for this field, but this lack of financial resources leads to the failure of these companies, so providing a model for predicting profits and losses in companies is very important and functional.Materials and Method: In this study, a combination of two logistic regression algorithms and differential analysis were used to design a profit and loss forecasting model. Also, the information of 20 companies in the field of health was used to evaluate the proposed model. 10 profitable companies and 10 loss-making companies were selected and for each company, nine variables independent of the financial information of these companies were collected.Results: The designed prediction model was implemented on the data in this study. To do this, the data were divided into two sets: training and testing. The prediction model was implemented on training data and evaluated by test data and reached 99.65% sensitivity, 94.75% specificity and 96.28% accuracy. The proposed model was then compared with the methods of decision tree C4.5, Bayesian, support vector machine, nearest neighborhood and multilayer neural network and it was found to have a better output.Conclusion: In this study, it was found that the risk in the field of health investment can be reduced, so the profit and loss situation of health companies can be predicted with appropriate accuracy. It was also found that the combination of logistic regression and differential analysis algorithms can increase the accuracy of the prediction model.
{"title":"Designing a Profit and Loss Prediction Model for Health Companies Using Data Mining","authors":"A. Abdolahi, V. Nowzari, A. Pirzad, S. Amirhosseini","doi":"10.30699/fhi.v10i1.305","DOIUrl":"https://doi.org/10.30699/fhi.v10i1.305","url":null,"abstract":"Introduction: Health companies need investment for development. Due to the high risk of their activities, it is very difficult to attract investment for this field, but this lack of financial resources leads to the failure of these companies, so providing a model for predicting profits and losses in companies is very important and functional.Materials and Method: In this study, a combination of two logistic regression algorithms and differential analysis were used to design a profit and loss forecasting model. Also, the information of 20 companies in the field of health was used to evaluate the proposed model. 10 profitable companies and 10 loss-making companies were selected and for each company, nine variables independent of the financial information of these companies were collected.Results: The designed prediction model was implemented on the data in this study. To do this, the data were divided into two sets: training and testing. The prediction model was implemented on training data and evaluated by test data and reached 99.65% sensitivity, 94.75% specificity and 96.28% accuracy. The proposed model was then compared with the methods of decision tree C4.5, Bayesian, support vector machine, nearest neighborhood and multilayer neural network and it was found to have a better output.Conclusion: In this study, it was found that the risk in the field of health investment can be reduced, so the profit and loss situation of health companies can be predicted with appropriate accuracy. It was also found that the combination of logistic regression and differential analysis algorithms can increase the accuracy of the prediction model.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127775044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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